• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2041-2049
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• 1998

This paper suggests a technique for program segmentation and maintenance using PSDG(Post-State Dependency Graph) that improves the quality of a software by identifying and detecting defects in already fixed source code. A program segmentation is performed by utilizing source code analysis which combines the measures of static, dynamic and semantic slicing when we need understandability of defect in programs for corrective maintanence. It provides users with a segmental principle to split a program by tracing state dependency of a source code with the graph, and clustering and highlighting, Through a modeling of the PSDG, elimination of ineffective program deadcode and generalization of related program segments arc possible, Additionally, it can be correlated with other design modeb as STD(State Transition Diagram), also be used as design documents.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1369-1376
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• 2012

In this paper, we propose 2D industrial image registration method for the detection of defects. Proposed method performs preprocessing to smooth the original image with the preservation of the edge for the robust registration against general noise. Then, x-direction gradient magnitude image and corresponding binary image are generated. Density analysis around neighborhood regions per pixel are performed to generate feature image for preventing mis-registration due to moire-like patterns, which frequently happen in industrial images. Finally, 2D image registration based on phase correlation between feature images is performed to calculate translational parameters to align two images rapidly and optimally. Experimental results showed that the registration accuracy of proposed method for the real industrial images was 100% and our method was about twenty times faster than the previous method. Our fast and accurate method could be used for the real industrial applications.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.755-760
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• 2007

자기누설(MFL) 데이터는 파이프 라인을 통해 이동하는 자기누설 피그에 의해서 얻어지는 데이터이다. 자기 누설 데이터 뿐만 아니라, 이들을 각종 기법으로 분석한 데이터 역시 분석가가 직접적으로 접근하기에는 너무 복잡하며, 원시 데이터를 가지고 파이프 라인의 결함을 빠른 시간 내에 찾아내고 그 원인을 유추하기란 매우 힘든 작업이다. 이러한 불편함을 해결해 주기 위한 소프트웨어의 개발은 사용자가 가상의 파이프 라인을 따라 항해하면서, 개개의 결함이나 두드러진 특징들이 파이프 라인의 어디에 위치하는지에 관한 정보를 보다 쉽고 직관적으로 인지할 수 있게 해준다. 비단 자기누설(MFL) 데이터뿐만 아니라 이와 같은 방대한 양의 데이터를 분석하는데 있어서 가시화 시스템의 역할은 상당히 중요하다. 본 논문에서는 대용량의 데이터를 가시화하는데 있어서 필요한 여러 고려 사항들을 소개하고 각 고려 사항에 대한 해결 방안을 제시한다.

• Journal of the Korean Magnetics Society
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• v.23 no.1
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• pp.18-25
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• 2013

MFL PIG (Magnetic Flux Leakage Pipeline Inspection Gauge) is called the system which detects the defect for underground pipelines by using magnetic flux leakage method in nondestructive testing. This method is very suitable for testing pipelines because pipeline has high magnetic permeability. MFL PIG generates the magnetic fields to the pipe axially oriented, and detect the signal of leakage flux by using hall sensor. However, MFL PIG is hard to detect the axially oriented crack with small size because the magnetic flux leakage is not enough to be occurred. To detect the small size and axially oriented crack, the circumferential MFL (CMFL) PIG is being proposed and it can maximize the leakage flux for the axial crack by performing magnetic fields circumferentially on the pipe. In this paper, CMFL PIG is applied to detect the axially oriented crack with small size and the analysis for the distribution and the amplitude of the leakage flux signal is performed by using three dimensional finite element method. From sensing signals, the method how to determine the shape of axially oriented cracks is proposed and verified with experiment.

• Smart Media Journal
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• v.11 no.11
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• pp.92-98
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• 2022

Identifying defects in textiles is a key procedure for quality control. This study attempted to create a model that detects defects by analyzing the images of the fabrics. The models used in the study were deep learning-based VGGNet and ResNet, and the defect detection performance of the two models was compared and evaluated. The accuracy of the VGGNet and the ResNet model was 0.859 and 0.893, respectively, which showed the higher accuracy of the ResNet. In addition, the region of attention of the model was derived by using the Grad-CAM algorithm, an eXplainable Artificial Intelligence (XAI) technique, to find out the location of the region that the deep learning model recognized as a defect in the fabric image. As a result, it was confirmed that the region recognized by the deep learning model as a defect in the fabric was actually defective even with the naked eyes. The results of this study are expected to reduce the time and cost incurred in the fabric production process by utilizing deep learning-based artificial intelligence in the defect detection of the textile industry.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.5
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• pp.510-517
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• 2001

This paper describes an initial study for the application of eddy current pattern recognition approaches to more realistic flaw characterization in steam generator tubes. For this purpose, finite-element model-based theoretical eddy current testing (ECT) signals are simulated from 5 types of OD flaws with the variation in flaw size parameters and testing frequency. In addition, three kinds of software are developed for the convenience in the application of steps in pattern recognition approaches such as feature extraction feature selection and classification by probabilistic neural networks (PNNs). The cross point of the ECT signals simulated from flaws with non-symmetric cross-sections shows the deviation from the origin of the impedance plane. New features taking advantages of this phenomenon are added to complete the feature set with a total of 18 features. Then, classification with PNNs are performed based on this feature set. The PNN classifiers show high performance for the identification of symmetry in the cross-section of a flaw. However, they show very limited success in the interrogation of the sharpness of flaw tips.

• Journal of the Korean Geotechnical Society
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• v.21 no.5
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• pp.207-214
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• 2005

The reflected signals from the defects of a pile and the boundaries between the pile and soils are analyzed to evaluate the integrity of drilled shafts in the impact-echo test. Signals varied according to both of the stiffness ratio of the pile to defects and that of the pile to surrounding soils. Model tests using the small size pile in the laboratory and numerical analyses have limitations in finding the characteristics of the signals due to different stress wave characteristics and unreliable modelling for the interaction between the pile and soils respectively. Full scale testing piles which have artificial defects are installed by the actual construction method and they were used to investigate the characteristics of reflected signals according to defects and the stiffness ratios of the pile to soils around.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.2
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• pp.40-47
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• 2008

It is known that the adhesive interface testing of the rocket motor using the ultrasonic wave is superior to the other testing methods about the ability to economical detect the defects. But, the signal analysis of the ultrasonic wave takes a lot of time and efforts because the time interval of the transmitted pulse and the received pulse is too short to separate the reflected signals due to the multi-layers of the rocket motor. The ultrasonic testing of rocket motor have only applied to the automatic system about extremely limited areas like the debond in adhesive interface between the motor case and the insulator. In this study the new technique to detect the debond between the liner and the propellant using the property of the resonance and the lamb waves instead of the existing ultrasonic testing was described.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.10
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• pp.1079-1088
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• 2003

In this paper, we propose a high-resolution time-frequency domain reflectometry technique as a methodology of detection and estimation of faults on a wire. This method adopts the time-frequency cross correlation characteristics of the observed signal in both time and frequency domains simultaneously. The accuracy of the proposed method is verified with experiments using a RG type coaxial cable and comparing it with traditional time domain as well as frequency domain reflectometry methods. It is clearly shown here that the proposed algorithm produces excellent results compared to the conventional methods for single as well as multiple fault cables.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.55-59
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• 2007

It is known that the adhesive interface testing of the rocket motor which using the ultrasonic wave iS superior to the other testing methods about the economically detectable abiliη of the defects. But, the signal analysis of the ultrasonic wave takes too much time and effort that the time interval of the transmitted pulse and the received pulse is too short to be separated the reflected signals because the structure of the rocket motor is multi-layers. The ultrasonic testing of rocket motor have been only applied with automatic system about extremely limited area like the debond in adhesive interface between the motor case and insulator. In this study the new technique to detect the debond between the liner and the propellant using the property of the resonance and Lamb waves was described as comparing the existence ultrasonic testing.